cantidad de celulares vistos desde que entro por primera vez hasta que realizo la primer compra.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Santi\\AppData\\Roaming\\Python\\Python36\\site-packages\\IPython\\core\\interactiveshell.py:2728: DtypeWarning: Columns (4) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>person</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>2018-03-20 17:46:12</td>\n",
       "      <td>00204059</td>\n",
       "      <td>Motorola Moto X2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2282</th>\n",
       "      <td>2018-04-26 22:28:53</td>\n",
       "      <td>00c13dee</td>\n",
       "      <td>Samsung Galaxy Core Plus Duos TV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2547</th>\n",
       "      <td>2018-06-10 14:37:50</td>\n",
       "      <td>00fdbb4b</td>\n",
       "      <td>Samsung Galaxy S6 Flat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4245</th>\n",
       "      <td>2018-03-16 13:50:25</td>\n",
       "      <td>0146a9df</td>\n",
       "      <td>iPhone 5s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6985</th>\n",
       "      <td>2018-02-09 22:07:07</td>\n",
       "      <td>01db2fe6</td>\n",
       "      <td>Samsung Galaxy J5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                timestamp    person                             model\n",
       "220   2018-03-20 17:46:12  00204059                  Motorola Moto X2\n",
       "2282  2018-04-26 22:28:53  00c13dee  Samsung Galaxy Core Plus Duos TV\n",
       "2547  2018-06-10 14:37:50  00fdbb4b            Samsung Galaxy S6 Flat\n",
       "4245  2018-03-16 13:50:25  0146a9df                         iPhone 5s\n",
       "6985  2018-02-09 22:07:07  01db2fe6                 Samsung Galaxy J5"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos = pd.read_csv('events.csv')\n",
    "datos_conversion_r = datos[datos[\"event\"] == \"conversion\"].drop_duplicates(\"person\")\n",
    "datos_conversion = datos_conversion_r[[\"timestamp\",\"person\",\"model\"]].dropna()\n",
    "datos_conversion.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>person</th>\n",
       "      <th>timestamp2</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00204059</td>\n",
       "      <td>2018-03-20 17:46:12</td>\n",
       "      <td>2018-01-31 17:01:46</td>\n",
       "      <td>iPhone 5s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00204059</td>\n",
       "      <td>2018-03-20 17:46:12</td>\n",
       "      <td>2018-01-31 17:03:56</td>\n",
       "      <td>iPhone 5c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00204059</td>\n",
       "      <td>2018-03-20 17:46:12</td>\n",
       "      <td>2018-02-01 23:16:46</td>\n",
       "      <td>Motorola Moto G3 4G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00204059</td>\n",
       "      <td>2018-03-20 17:46:12</td>\n",
       "      <td>2018-02-02 17:39:39</td>\n",
       "      <td>Motorola Moto G4 Play DTV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00204059</td>\n",
       "      <td>2018-03-20 17:46:12</td>\n",
       "      <td>2018-02-06 02:31:44</td>\n",
       "      <td>Asus Zenfone 2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     person           timestamp2            timestamp  \\\n",
       "0  00204059  2018-03-20 17:46:12  2018-01-31 17:01:46   \n",
       "1  00204059  2018-03-20 17:46:12  2018-01-31 17:03:56   \n",
       "2  00204059  2018-03-20 17:46:12  2018-02-01 23:16:46   \n",
       "3  00204059  2018-03-20 17:46:12  2018-02-02 17:39:39   \n",
       "4  00204059  2018-03-20 17:46:12  2018-02-06 02:31:44   \n",
       "\n",
       "                       model  \n",
       "0                  iPhone 5s  \n",
       "1                  iPhone 5c  \n",
       "2        Motorola Moto G3 4G  \n",
       "3  Motorola Moto G4 Play DTV  \n",
       "4             Asus Zenfone 2  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos_viewed_product_r = datos[datos[\"event\"] == \"viewed product\"].drop_duplicates()\n",
    "datos_viewed_product = datos_viewed_product_r[[\"timestamp\",\"person\",\"model\"]].drop_duplicates(subset=[\"person\",\"model\"], keep='first')\n",
    "\n",
    "gente_compro = datos_conversion[[\"person\",\"timestamp\"]].dropna()\n",
    "gente_compro.columns = [\"person\",\"timestamp2\"]\n",
    "\n",
    "vistos_gente_compro = pd.merge(gente_compro, datos_viewed_product, how='left', on='person')\n",
    "vistos_gente_compro.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "vistos_gente_compro['timestamp'] = pd.to_datetime(vistos_gente_compro['timestamp'])\n",
    "vistos_gente_compro['timestamp2'] = pd.to_datetime(vistos_gente_compro['timestamp2'])\n",
    "\n",
    "vistos_gente_compro[\"tiempo en comprar\"] = pd.to_numeric(vistos_gente_compro[\"timestamp2\"] - vistos_gente_compro[\"timestamp\"])\n",
    "vistos_gente_compro_=vistos_gente_compro.index[vistos_gente_compro[\"tiempo en comprar\"] > 0]\n",
    "vistos_gente_compro_antes = vistos_gente_compro.loc[vistos_gente_compro_]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>timestamp2</th>\n",
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       "    <tr>\n",
       "      <th>person</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>00204059</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00c13dee</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0146a9df</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01db2fe6</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03bdcd81</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
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      ],
      "text/plain": [
       "          timestamp  timestamp2\n",
       "person                         \n",
       "00204059          8           8\n",
       "00c13dee          6           6\n",
       "0146a9df          1           1\n",
       "01db2fe6          3           3\n",
       "03bdcd81          4           4"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vistos = vistos_gente_compro_antes.groupby('person').agg({'timestamp':'count','timestamp2':'count'})\n",
    "vistos.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>timestamp</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(0 - 1)</th>\n",
       "      <td>454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2 - 13)</th>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(14 - 40)</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(41 - 67)</th>\n",
       "      <td>4</td>\n",
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      ],
      "text/plain": [
       "           timestamp\n",
       "timestamp           \n",
       "(0 - 1)          454\n",
       "(2 - 13)          51\n",
       "(14 - 40)          6\n",
       "(41 - 67)          4"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos_4_bins = vistos.groupby(pd.cut(vistos[\"timestamp\"], 4, labels = ('(0 - 1)','(2 - 13)', '(14 - 40)','(41 - 67)'))).count()\n",
    "datos_4_bins = datos_4_bins.drop([\"timestamp2\"],axis='columns')\n",
    "datos_4_bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x417.6 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.logspace(0, 5, num=21)\n",
    "y = (np.sin(1.e-2*(x[:-1]-20))+3)**10\n",
    "\n",
    "p = datos_4_bins.plot(kind='bar',    # Plot a bar chart\n",
    "        legend=False,    # Turn the Legend off\n",
    "        width=0.75,      # Set bar width as 75% of space available\n",
    "        figsize=(8,5.8),  # Set size of plot in inches\n",
    "        color=[plt.cm.Paired(np.arange(len(vistos)))],\n",
    "        logy=True)\n",
    "\n",
    "p.set_xlabel(\"Cantidad de celulares que vieron antes de comprar\", size = 16)\n",
    "p.set_ylabel(\"Cantidad de personas\", size = 16)\n",
    "p.set_title(\"Cantidad de personas vs. cantidad de celulares que vieron antes de comprar\", size = 22)\n",
    "\n",
    "for i in p.patches:\n",
    "    # get_width pulls left or right; get_y pushes up or down\n",
    "    p.text (i.get_x()+.25, (103*i.get_height()/100), i.get_height(), fontsize=15, color='dimgrey')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}